I’ve mentioned @recallnet a few times already But here’s a quick breakdown of what they’re actually building: What is Recall? A reputation protocol for the growing “Internet of Agents” Built for a future where billions of AI agents interact with each other and with us Solving discovery and trust through performance, not marketing Key features: AgentRank: Onchain reputation system based on real-world, verifiable performance AI Competitions: Agents compete live to prove their skills and keep rankings up to date Curation Markets: Users stake on agents they believe will perform well and earn if they’re right Skill Pools: Communities stake on specific AI skills to guide agent development toward real demand All powered by $RECALL the whole ecosystem - Rewards top agents, early curators, and useful contributors Aligns incentives to keep the reputation system trustworthy They're building the foundation for how autonomous agents will coordinate in the future.

Recall was invested by 40 institutions, and this is not the end, the lead investment was Multicoin, which was invested by him equivalent to half of the project's foot into Binance You may not be familiar with this institution, but Solana, APT, Lido, and Zama, which has been very popular in the past two days, and Multicoin are the financiers behind it It can be seen that Multicoin's investment vision is very tricky, and I wonder what kind of team Recall can get the investment from this T1 institution I went through everyone's resumes and found that the core team was from ConsenSys (MetaMask and Linea's parent company), and from Apple, yes, Jobs's Apple! › ••••••••• ‹ Today, let's introduce the core team of Recall, and before we begin, we will introduce the Recal project 👇 @recallnet can be seen as a martial arts hall, each robot is a recruit egg, each player is the corresponding master, you teach the martial arts cheats to your apprentice AI robot, the robot learns your martial arts to kill the enemy on the battlefield, and you can receive a bounty for killing the enemy in battle The higher the martial arts you teach, the stronger your apprentices, the more enemies you kill, and the more bounties you receive, which is your reward as a master. If you teach the Ink Splash Fist, then the calf will be finished, and you will fall down before you can kill the enemy As abstract as it sounds, this is the AI proxy trading hackathon that Recall is currently running, where everyone can train their own AI agent to operate in an on-chain simulated trading environment Each robot fights in a simulated trading environment, and who can win in the end depends on which trading strategy (martial arts cheats) is stronger Recall creates a leaderboard based on the number of kills, scoring each robot based on how well it performs in the actual battle. This scoring system is called AgentRank, and unlike traditional competitions, it does not rely on judges to score, but ranks based on real execution records on the chain The judgment, execution results, and profit and loss curves of each transaction of the robot are all on the chain, the data is open source, the results are transparent, and it is clear who is strong and who is weak In the future, the product will be officially launched, and I can't imagine how easy it will be to use, which is much more practical than simply taking AI Proxy 1 to set up a simple regular investment. We can customize our own strategy, or choose martial arts masters according to the leaderboard, in fact, this is similar to the exchange with orders, but what is better than the exchange is that it is completely AI to lead orders, and every order on the chain can be checked! Recall is building a world where thinking robots can become a new species on the chain, and everyone can become a generation of grandmasters › ••••••••• ‹ 📌 Moving on to Recall's core team ▪️Andrew W. Hill (Co-Founder & CEO) @andrewxhill Ph.D., his research direction is ecosystem modeling, and later he switched to technology. He was one of the few people who moved from a natural science background to distributed systems to the end Textile (previous project) initially focused on decentralized data processing tools, and he led the development of IPFS Buckets and Filecoin storage layer interfaces, and also participated in the design of modules such as go-threads and Powergate. These are not fast-paced projects that "issue coins and pull disks", but build core tools that Web3 can use for a long time From Filecoin's data store, to Tableland's SQL structure, to Recall's AgentRank mechanism, Andrew enjoys working with sustainable systems ▪️Sander Pick (Co-Founder & CTO) @sanderpick was previously an engineer in Apple's Special Projects team and was one of the early contributors to the Filecoin technology stack You may not have used his Powergate, but if you have used Filecoin, you've already used his work. He also wrote go-threads, a component that provides data synchronization for IPFS, and many decentralized databases are now designed with reference to its structure It can be said that Textile can survive for five years and write the data stack completely because there are core engineers like Sander who have connected the system layer by layer. He is now the CTO of Recall, responsible for the on-chain logic behind the proxy system, and is the key executor of the system ▪️Michael Sena (Co-Founder) @dataliquidity is the original team of Ceramic Network. Ceramic is an on-chain state layer system that implements a state system that "data does not stay in the wallet, but is updated in real time" Michael spent many years at ConsenSys (MetaMask's parent company) and was one of the creators of Uport, Ethereum's early DID system. In 2018, he began to lead 3Box, and later made Ceramic, which standardized on-chain data flow into a product ▪️Danny Zuckerman (Head of Recall Products) @dazuck has worked with Michael on 3Box, as well as strategy and product at ConsenSys, and he is the most familiar with "how to turn a system into a product that developers can plug into". Danny doesn't write code, but he sorts out a lot of product processes, documentation systems, and module structures You can see that Recall's SDK documentation is very complete, the test path is clear, the competition mechanism is reasonable, and most of it is the product process that he pulls ▪️Carson Farmer (Head of Research) @carsonfarmer is a low-key but extremely central researcher. What he's working on is the intersection of "distributed systems + AI inference". In other words, there are many projects on the market that "AI agent sends a task and ends it", and he is concerned about how the agent behavior itself is verified by the chain, how it is scored, and whether it can be combined and reused He has a lot of data on GitHub and Docs about AgentRank, task history, and on-chain behavior structured design, which is the foundation of this project's "ability to run for a long time". You could say he's the one responsible for turning "AI looks like it works" into "it's actually available on-chain", dealing with deep logic that you wouldn't have discovered without looking at the source code @MsEggmily Finally Recall has super beauties inside!! Hurry up and pay attention